A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

This is the software I like to have on various platforms, and is a work in progress (and so may morph wildly over time according to my personal whims) that I've started mostly for my own use. Recently I used this list in rebuilding my personal workstation after an awful day. Documentation saves (as do backups)!

My personal workstation and laptop are currently running Linux Mint 22.3 (Zena, based on Ubuntu 24.04), moderately tailored for my own use. Previously, I was on Ubuntu 22.04 LTS Desktop. For me, Mint requires considerably less customization to be tolerable, and that made switching to it a lot less painful than it otherwise could have been.

I prefer distro packages where available and not too far out of date, then the latest vendor official packages or binaries when I can can find them (except where it would be too disruptive: like when they overwrite critical dependencies). I will turn to vendor AppImages where packages

You know that specific kind of developer frustration where you've got four browser tabs open — one for OpenAI billing, one for Anthropic credits, one for DeepSeek, and one you forgot about entirely — and you're just trying to run a quick experiment? Yeah. That's the exact problem AgentRouter showed up to fix.

Launched in October 2025, AgentRouter (agentrouter.org) is a non-profit AI API gateway that routes your requests to multiple top-tier AI providers — Anthropic, OpenAI, DeepSeek, Zhipu AI — through a single API key and a single base URL. It's OpenAI-compatible, which means any tool that talks to OpenAI can be pointed at AgentRouter with one config change. And right now, signing up through a referral link gets you $200 in free credits before you've spent a single dollar.

👉 Grab your $200 in free credits here

• Raspberry Pi 3/4
• USB Printer
• 8GB+ microSD card
• 2+ Amp Micro-USB power supply (Pi 3) or 3+ Amp USB-C power supply (Pi 4)

If you're smart, you'll use the 64-bit Lite image, but I didn't find that at first and went with the 64-bit Desktop image. This meant more work cleaning up unnecessary packages.

Just a somewhat chaotic list of Proxmox VE related tips/notes.

Please note that unless you see a shebang (#!/...) these code blocks are usually meant to be copy & pasted directly into the shell. Some of the steps will not work if you run part of them in a script and copy paste other ones as they rely on variables set before.
The { and } surrounding some scripts are meant to avoid cluttering your shell history with individual commands, etc. You can ignore them if you manually copy paste the individual commands.

• Proxmox VE tips
• Table of contents

1. Initial build request Build a pixel-perfect Galaga arcade game in a single self-contained HTML file with inline CSS and JS. Replicate the original 1981 Namco arcade cabinet experience with full fidelity to the original rules, sprite designs, and mechanics.

2. Fidelity review (with a reference screenshot) please review sprites and how ships attack, because galaga was different [Image #1] here an example. But search on internet for more details (accompanied by the screenshot)

3. Layout bug fix The Galaga on top of the screen is not visibile on the browser page, please fix this

4. Cabinet control animation request

Update all outdated dependencies in this Elixir project, handling both safe updates and breaking changes.

Run mix hex.outdated to get a list of all outdated dependencies. Parse the output to identify:

• Safe updates: Dependencies where only the patch or minor version has changed (no major version bump)